import h3

# Function to generate H3 index from latitude and longitude
def generate_h3_index(lat, lon, resolution):
    return h3.geo_to_h3(lat, lon, resolution)

# Example usage
latitude = 37.7749
longitude = -122.4194
resolution = 9
h3_index = generate_h3_index(latitude, longitude, resolution)
print(f"H3 Index: {h3_index}")
# Function to generate H3 index from latitude and longitude
def generate_h3_index(lat, lon, resolution):
    return h3.geo_to_h3(lat, lon, resolution)
    import pandas as pd
    import folium
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler, OneHotEncoder
    from sklearn.compose import ColumnTransformer
    from sklearn.pipeline import Pipeline
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.metrics import mean_squared_error, r2_score

    def predict_revenue(data):
        # Assuming data is a pandas DataFrame with columns:
        # 'sqft', 'population', 'latitude', 'longitude', 'category', 'revenue'

        # Separate features and target
        X = data.drop('revenue', axis=1)
        y = data['revenue']

        # Split the data into training and testing sets
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

        # Define preprocessing steps
        numeric_features = ['sqft', 'population', 'latitude', 'longitude']
        categorical_features = ['category']

        preprocessor = ColumnTransformer(
            transformers=[
                ('num', StandardScaler(), numeric_features),
                ('cat', OneHotEncoder(drop='first', sparse=False), categorical_features)
            ])

        # Create a pipeline with preprocessor and RandomForestRegressor
        model = Pipeline([
            ('preprocessor', preprocessor),
            ('regressor', RandomForestRegressor(n_estimators=100, random_state=42))
        ])

        # Fit the model
        model.fit(X_train, y_train)

        # Make predictions
        y_pred = model.predict(X_test)

        # Evaluate the model
        mse = mean_squared_error(y_test, y_pred)
        r2 = r2_score(y_test, y_pred)

        print(f"Mean Squared Error: {mse}")
        print(f"R-squared Score: {r2}")

        return model

    def create_map(data, model):
        # Make predictions for all data points
        predicted_revenue = model.predict(data.drop('revenue', axis=1))
        
        # Add predictions to the dataframe
        data['predicted_revenue'] = predicted_revenue

        # Create a map centered on the mean latitude and longitude
        map_center = [data['latitude'].mean(), data['longitude'].mean()]
        m = folium.Map(location=map_center, zoom_start=10)

        # Add markers for each building
        for idx, row in data.iterrows():
            popup_text = f"""
            Category: {row['category']}
            Sqft: {row['sqft']}
            Population: {row['population']}
            Actual Revenue: ${row['revenue']:,.2f}
            Predicted Revenue: ${row['predicted_revenue']:,.2f}
            """
            folium.Marker(
                location=[row['latitude'], row['longitude']],
                popup=popup_text,
                tooltip=f"Building {idx}"
            ).add_to(m)

        return m

    # Example usage
    # Assuming you have a CSV file with the required columns
    # data = pd.read_csv('building_data.csv')
    # trained_model = predict_revenue(data)
    # map_with_predictions = create_map(data, trained_model)
    # map_with_predictions.save('building_revenue_map.html')


    def augment_llm_with_domain_content(llm, data_sources):
        """
        Augment a language model with domain-specific content from various sources.
        
        :param llm: The base language model to augment
        :param data_sources: A dictionary containing different types of data sources
        :return: An augmented language model
        """
        # SQL Database integration
        if 'sql_db' in data_sources:
            db_connection = data_sources['sql_db']
            relevant_data = extract_relevant_data_from_sql(db_connection)
            llm = fine_tune_with_sql_data(llm, relevant_data)
        
        # Document processing
        if 'documents' in data_sources:
            doc_paths = data_sources['documents']
            processed_docs = process_documents(doc_paths)
            llm = fine_tune_with_document_data(llm, processed_docs)
        
        # Table data integration
        if 'tables' in data_sources:
            table_data = data_sources['tables']
            structured_data = process_table_data(table_data)
            llm = fine_tune_with_structured_data(llm, structured_data)
        
        # Spatial dataset integration
        if 'spatial_data' in data_sources:
            spatial_dataset = data_sources['spatial_data']
            processed_spatial_data = process_spatial_data(spatial_dataset)
            llm = fine_tune_with_spatial_data(llm, processed_spatial_data)
        
        return llm

    def extract_relevant_data_from_sql(db_connection):
        # Implementation to extract and process SQL data
        pass

    def process_documents(doc_paths):
        # Implementation to process and extract information from documents
        pass

    def process_table_data(table_data):
        # Implementation to process structured table data
        pass

    def process_spatial_data(spatial_dataset):
        # Implementation to process spatial data
        pass

    def fine_tune_with_sql_data(llm, sql_data):
        # Implementation to fine-tune LLM with SQL data
        return llm

    def fine_tune_with_document_data(llm, doc_data):
        # Implementation to fine-tune LLM with document data
        return llm

    def fine_tune_with_structured_data(llm, structured_data):
        # Implementation to fine-tune LLM with structured data
        return llm

    def fine_tune_with_spatial_data(llm, spatial_data):
        # Implementation to fine-tune LLM with spatial data
        return llm

    # Example usage
    # base_llm = load_base_language_model()
    # data_sources = {
    #     'sql_db': sql_connection,
    #     'documents': ['path/to/doc1.pdf', 'path/to/doc2.txt'],
    #     'tables': [df1, df2],
    #     'spatial_data': geopandas_dataframe
    # }
    # augmented_llm = augment_llm_with_domain_content(base_llm, data_sources)